Overview

Dataset statistics

Number of variables12
Number of observations792
Missing cells6
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.4 KiB
Average record size in memory96.2 B

Variable types

Numeric7
Categorical5

Alerts

+ 1 Mbps - 6 Mbps has a high cardinality: 783 distinct valuesHigh cardinality
+ 30 Mbps has a high cardinality: 503 distinct valuesHigh cardinality
Total has a high cardinality: 787 distinct valuesHigh cardinality
Año is highly overall correlated with OTROSHigh correlation
OTROS is highly overall correlated with AñoHigh correlation
Provincia is uniformly distributedUniform
+ 1 Mbps - 6 Mbps is uniformly distributedUniform
Total is uniformly distributedUniform
+ 512 Kbps - 1 Mbps has 50 (6.3%) zerosZeros
+ 6 Mbps - 10 Mbps has 38 (4.8%) zerosZeros
+ 10 Mbps - 20 Mbps has 71 (9.0%) zerosZeros
+ 20 Mbps - 30 Mbps has 104 (13.1%) zerosZeros
OTROS has 449 (56.7%) zerosZeros

Reproduction

Analysis started2023-01-03 16:52:35.113411
Analysis finished2023-01-03 16:52:40.125128
Duration5.01 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Año
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.6364
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-01-03T11:52:40.187418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2021
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3860299
Coefficient of variation (CV)0.0011825867
Kurtosis-1.1859202
Mean2017.6364
Median Absolute Deviation (MAD)2
Skewness0.031978903
Sum1597968
Variance5.6931387
MonotonicityDecreasing
2023-01-03T11:52:40.260813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 96
12.1%
2020 96
12.1%
2019 96
12.1%
2018 96
12.1%
2017 96
12.1%
2016 96
12.1%
2015 96
12.1%
2014 96
12.1%
2022 24
 
3.0%
ValueCountFrequency (%)
2014 96
12.1%
2015 96
12.1%
2016 96
12.1%
2017 96
12.1%
2018 96
12.1%
2019 96
12.1%
2020 96
12.1%
2021 96
12.1%
2022 24
 
3.0%
ValueCountFrequency (%)
2022 24
 
3.0%
2021 96
12.1%
2020 96
12.1%
2019 96
12.1%
2018 96
12.1%
2017 96
12.1%
2016 96
12.1%
2015 96
12.1%
2014 96
12.1%

Trimestre
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
1
216 
4
192 
3
192 
2
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters792
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Length

2023-01-03T11:52:40.341582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T11:52:40.432716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring characters

ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 216
27.3%
4 192
24.2%
3 192
24.2%
2 192
24.2%

Provincia
Categorical

Distinct24
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
Buenos Aires
 
33
Capital Federal
 
33
Tierra Del Fuego
 
33
Santiago Del Estero
 
33
Santa Fe
 
33
Other values (19)
627 

Length

Max length19
Median length15
Mean length8.9166667
Min length5

Characters and Unicode

Total characters7062
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuenos Aires
2nd rowCapital Federal
3rd rowCatamarca
4th rowChaco
5th rowChubut

Common Values

ValueCountFrequency (%)
Buenos Aires 33
 
4.2%
Capital Federal 33
 
4.2%
Tierra Del Fuego 33
 
4.2%
Santiago Del Estero 33
 
4.2%
Santa Fe 33
 
4.2%
Santa Cruz 33
 
4.2%
San Luis 33
 
4.2%
San Juan 33
 
4.2%
Salta 33
 
4.2%
Río Negro 33
 
4.2%
Other values (14) 462
58.3%

Length

2023-01-03T11:52:40.514088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
santa 66
 
5.3%
la 66
 
5.3%
del 66
 
5.3%
san 66
 
5.3%
entre 33
 
2.6%
rioja 33
 
2.6%
pampa 33
 
2.6%
jujuy 33
 
2.6%
formosa 33
 
2.6%
buenos 33
 
2.6%
Other values (24) 792
63.2%

Most occurring characters

ValueCountFrequency (%)
a 924
 
13.1%
e 561
 
7.9%
o 495
 
7.0%
462
 
6.5%
n 429
 
6.1%
u 429
 
6.1%
r 429
 
6.1%
t 330
 
4.7%
s 297
 
4.2%
i 297
 
4.2%
Other values (30) 2409
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5346
75.7%
Uppercase Letter 1254
 
17.8%
Space Separator 462
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 924
17.3%
e 561
10.5%
o 495
9.3%
n 429
8.0%
u 429
8.0%
r 429
8.0%
t 330
 
6.2%
s 297
 
5.6%
i 297
 
5.6%
l 165
 
3.1%
Other values (15) 990
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 231
18.4%
S 198
15.8%
F 132
10.5%
L 99
7.9%
R 99
7.9%
N 66
 
5.3%
M 66
 
5.3%
J 66
 
5.3%
E 66
 
5.3%
D 66
 
5.3%
Other values (4) 165
13.2%
Space Separator
ValueCountFrequency (%)
462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6600
93.5%
Common 462
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 924
14.0%
e 561
 
8.5%
o 495
 
7.5%
n 429
 
6.5%
u 429
 
6.5%
r 429
 
6.5%
t 330
 
5.0%
s 297
 
4.5%
i 297
 
4.5%
C 231
 
3.5%
Other values (29) 2178
33.0%
Common
ValueCountFrequency (%)
462
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6897
97.7%
None 165
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 924
13.4%
e 561
 
8.1%
o 495
 
7.2%
462
 
6.7%
n 429
 
6.2%
u 429
 
6.2%
r 429
 
6.2%
t 330
 
4.8%
s 297
 
4.3%
i 297
 
4.3%
Other values (26) 2244
32.5%
None
ValueCountFrequency (%)
í 66
40.0%
ó 33
20.0%
é 33
20.0%
á 33
20.0%

HASTA 512 kbps
Real number (ℝ)

Distinct366
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.92396
Minimum1.007
Maximum998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-01-03T11:52:40.620192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.007
5-th percentile1.52695
Q19.3645
median48
Q3135.5
95-th percentile568
Maximum998
Range996.993
Interquartile range (IQR)126.1355

Descriptive statistics

Standard deviation190.10209
Coefficient of variation (CV)1.5096578
Kurtosis5.396598
Mean125.92396
Median Absolute Deviation (MAD)43.489
Skewness2.2997132
Sum99731.774
Variance36138.803
MonotonicityNot monotonic
2023-01-03T11:52:40.718987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 20
 
2.5%
15 16
 
2.0%
10 15
 
1.9%
16 15
 
1.9%
18 14
 
1.8%
8 12
 
1.5%
67 12
 
1.5%
26 11
 
1.4%
6 11
 
1.4%
71 10
 
1.3%
Other values (356) 656
82.8%
ValueCountFrequency (%)
1.007 1
 
0.1%
1.009 1
 
0.1%
1.01 3
 
0.4%
1.011 1
 
0.1%
1.053 1
 
0.1%
1.058 1
 
0.1%
1.063 1
 
0.1%
1.107 1
 
0.1%
1.11 8
1.0%
1.119 1
 
0.1%
ValueCountFrequency (%)
998 1
 
0.1%
991 1
 
0.1%
986 1
 
0.1%
973 1
 
0.1%
959 2
0.3%
958 3
0.4%
852 1
 
0.1%
847 1
 
0.1%
840 1
 
0.1%
791 1
 
0.1%

+ 512 Kbps - 1 Mbps
Real number (ℝ)

Distinct596
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.393794
Minimum0
Maximum995
Zeros50
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-01-03T11:52:40.826643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.4915
median8.8945
Q384
95-th percentile554
Maximum995
Range995
Interquartile range (IQR)80.5085

Descriptive statistics

Standard deviation198.51095
Coefficient of variation (CV)2.03823
Kurtosis7.1758007
Mean97.393794
Median Absolute Deviation (MAD)7.5845
Skewness2.7260485
Sum77135.885
Variance39406.597
MonotonicityNot monotonic
2023-01-03T11:52:40.934728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
 
6.3%
1 12
 
1.5%
4 10
 
1.3%
285 8
 
1.0%
97 8
 
1.0%
327 7
 
0.9%
109 7
 
0.9%
909 6
 
0.8%
112 6
 
0.8%
466 5
 
0.6%
Other values (586) 673
85.0%
ValueCountFrequency (%)
0 50
6.3%
1 12
 
1.5%
1.027 1
 
0.1%
1.058 1
 
0.1%
1.062 1
 
0.1%
1.077 1
 
0.1%
1.099 1
 
0.1%
1.123 1
 
0.1%
1.164 1
 
0.1%
1.169 1
 
0.1%
ValueCountFrequency (%)
995 1
 
0.1%
987 1
 
0.1%
974 1
 
0.1%
940 2
 
0.3%
928 2
 
0.3%
909 6
0.8%
908 1
 
0.1%
900 1
 
0.1%
896 1
 
0.1%
894 1
 
0.1%

+ 1 Mbps - 6 Mbps
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct783
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
14.014
 
3
35.409
 
3
22.409
 
2
58.588
 
2
30.727
 
2
Other values (778)
780 

Length

Max length9
Median length6
Mean length6.2714646
Min length5

Characters and Unicode

Total characters4967
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique776 ?
Unique (%)98.0%

Sample

1st row313.382
2nd row39.918
3rd row4.386
4th row16.888
5th row61.369

Common Values

ValueCountFrequency (%)
14.014 3
 
0.4%
35.409 3
 
0.4%
22.409 2
 
0.3%
58.588 2
 
0.3%
30.727 2
 
0.3%
40.285 2
 
0.3%
28.600 2
 
0.3%
415.075 1
 
0.1%
22.246 1
 
0.1%
87.994 1
 
0.1%
Other values (773) 773
97.6%

Length

2023-01-03T11:52:41.022988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14.014 3
 
0.4%
35.409 3
 
0.4%
22.409 2
 
0.3%
58.588 2
 
0.3%
30.727 2
 
0.3%
40.285 2
 
0.3%
28.600 2
 
0.3%
165.922 1
 
0.1%
22.141 1
 
0.1%
24.113 1
 
0.1%
Other values (773) 773
97.6%

Most occurring characters

ValueCountFrequency (%)
. 828
16.7%
1 498
10.0%
2 485
9.8%
4 460
9.3%
3 451
9.1%
5 400
8.1%
8 396
8.0%
6 387
7.8%
0 363
7.3%
7 357
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4139
83.3%
Other Punctuation 828
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 498
12.0%
2 485
11.7%
4 460
11.1%
3 451
10.9%
5 400
9.7%
8 396
9.6%
6 387
9.4%
0 363
8.8%
7 357
8.6%
9 342
8.3%
Other Punctuation
ValueCountFrequency (%)
. 828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4967
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 828
16.7%
1 498
10.0%
2 485
9.8%
4 460
9.3%
3 451
9.1%
5 400
8.1%
8 396
8.0%
6 387
7.8%
0 363
7.3%
7 357
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 828
16.7%
1 498
10.0%
2 485
9.8%
4 460
9.3%
3 451
9.1%
5 400
8.1%
8 396
8.0%
6 387
7.8%
0 363
7.3%
7 357
7.2%

+ 6 Mbps - 10 Mbps
Real number (ℝ)

Distinct709
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.779756
Minimum0
Maximum917
Zeros38
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-01-03T11:52:41.114001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.18675
median19.966
Q363.17025
95-th percentile325.9158
Maximum917
Range917
Interquartile range (IQR)57.9835

Descriptive statistics

Standard deviation143.43138
Coefficient of variation (CV)1.9707593
Kurtosis12.961731
Mean72.779756
Median Absolute Deviation (MAD)16.77
Skewness3.4420732
Sum57641.567
Variance20572.561
MonotonicityNot monotonic
2023-01-03T11:52:41.216186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
4.8%
2 12
 
1.5%
1 4
 
0.5%
11 4
 
0.5%
655 3
 
0.4%
15 3
 
0.4%
1.337 2
 
0.3%
2.867 2
 
0.3%
14.101 2
 
0.3%
31 2
 
0.3%
Other values (699) 720
90.9%
ValueCountFrequency (%)
0 38
4.8%
1 4
 
0.5%
1.034 1
 
0.1%
1.066 1
 
0.1%
1.133 1
 
0.1%
1.165 1
 
0.1%
1.227 1
 
0.1%
1.311 1
 
0.1%
1.314 1
 
0.1%
1.321 1
 
0.1%
ValueCountFrequency (%)
917 1
0.1%
902 1
0.1%
858 1
0.1%
855 1
0.1%
849 1
0.1%
792 1
0.1%
784 1
0.1%
779 1
0.1%
778 1
0.1%
775 1
0.1%

+ 10 Mbps - 20 Mbps
Real number (ℝ)

Distinct678
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.715126
Minimum0
Maximum978
Zeros71
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-01-03T11:52:41.327589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.58225
median14.852
Q356.48
95-th percentile524.197
Maximum978
Range978
Interquartile range (IQR)51.89775

Descriptive statistics

Standard deviation174.83017
Coefficient of variation (CV)2.1136421
Kurtosis9.4745775
Mean82.715126
Median Absolute Deviation (MAD)12.6925
Skewness3.0747897
Sum65510.38
Variance30565.59
MonotonicityNot monotonic
2023-01-03T11:52:41.421511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
9.0%
1 5
 
0.6%
5 4
 
0.5%
119 3
 
0.4%
10 3
 
0.4%
100 3
 
0.4%
531 2
 
0.3%
641 2
 
0.3%
111 2
 
0.3%
22.91 2
 
0.3%
Other values (668) 695
87.8%
ValueCountFrequency (%)
0 71
9.0%
1 5
 
0.6%
1.061 1
 
0.1%
1.062 1
 
0.1%
1.076 1
 
0.1%
1.085 1
 
0.1%
1.162 1
 
0.1%
1.172 1
 
0.1%
1.202 1
 
0.1%
1.203 1
 
0.1%
ValueCountFrequency (%)
978 1
0.1%
966 1
0.1%
965 1
0.1%
958 1
0.1%
956 1
0.1%
920 1
0.1%
888 1
0.1%
886.678 1
0.1%
878 1
0.1%
832 1
0.1%

+ 20 Mbps - 30 Mbps
Real number (ℝ)

Distinct540
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.58282
Minimum0
Maximum997
Zeros104
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2023-01-03T11:52:41.515116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10.402
Q365.54025
95-th percentile623
Maximum997
Range997
Interquartile range (IQR)63.54025

Descriptive statistics

Standard deviation207.78423
Coefficient of variation (CV)2.0658025
Kurtosis6.1517716
Mean100.58282
Median Absolute Deviation (MAD)10.402
Skewness2.6103235
Sum79661.59
Variance43174.286
MonotonicityNot monotonic
2023-01-03T11:52:41.620349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
 
13.1%
1 24
 
3.0%
5 18
 
2.3%
2 13
 
1.6%
3 8
 
1.0%
4 7
 
0.9%
22 5
 
0.6%
29 5
 
0.6%
17 4
 
0.5%
41 4
 
0.5%
Other values (530) 600
75.8%
ValueCountFrequency (%)
0 104
13.1%
1 24
 
3.0%
1.032 1
 
0.1%
1.033 1
 
0.1%
1.068 1
 
0.1%
1.073 1
 
0.1%
1.084 1
 
0.1%
1.091 1
 
0.1%
1.135 1
 
0.1%
1.136 1
 
0.1%
ValueCountFrequency (%)
997 1
0.1%
991 1
0.1%
979 1
0.1%
977 1
0.1%
969 1
0.1%
964 1
0.1%
961 1
0.1%
949.093 1
0.1%
941 1
0.1%
899 1
0.1%

+ 30 Mbps
Categorical

Distinct503
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
0
112 
2
 
39
1
 
19
3
 
15
4
 
14
Other values (498)
593 

Length

Max length9
Median length6
Mean length3.6376263
Min length1

Characters and Unicode

Total characters2881
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique457 ?
Unique (%)57.7%

Sample

1st row3.381.049
2nd row1.188.072
3rd row35.715
4th row62.946
5th row14.055

Common Values

ValueCountFrequency (%)
0 112
 
14.1%
2 39
 
4.9%
1 19
 
2.4%
3 15
 
1.9%
4 14
 
1.8%
10 13
 
1.6%
5 9
 
1.1%
22 8
 
1.0%
9 7
 
0.9%
7 6
 
0.8%
Other values (493) 550
69.4%

Length

2023-01-03T11:52:41.724340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 112
 
14.1%
2 39
 
4.9%
1 19
 
2.4%
3 15
 
1.9%
4 14
 
1.8%
10 13
 
1.6%
5 9
 
1.1%
22 8
 
1.0%
9 7
 
0.9%
13 6
 
0.8%
Other values (493) 550
69.4%

Most occurring characters

ValueCountFrequency (%)
. 382
13.3%
1 376
13.1%
2 336
11.7%
0 300
10.4%
3 230
8.0%
4 228
7.9%
7 219
7.6%
6 216
7.5%
9 204
7.1%
5 197
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2499
86.7%
Other Punctuation 382
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 376
15.0%
2 336
13.4%
0 300
12.0%
3 230
9.2%
4 228
9.1%
7 219
8.8%
6 216
8.6%
9 204
8.2%
5 197
7.9%
8 193
7.7%
Other Punctuation
ValueCountFrequency (%)
. 382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 382
13.3%
1 376
13.1%
2 336
11.7%
0 300
10.4%
3 230
8.0%
4 228
7.9%
7 219
7.6%
6 216
7.5%
9 204
7.1%
5 197
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 382
13.3%
1 376
13.1%
2 336
11.7%
0 300
10.4%
3 230
8.0%
4 228
7.9%
7 219
7.6%
6 216
7.5%
9 204
7.1%
5 197
6.8%

OTROS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)32.4%
Missing6
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean26.008611
Minimum-1.945
Maximum898
Zeros449
Zeros (%)56.7%
Negative2
Negative (%)0.3%
Memory size6.3 KiB
2023-01-03T11:52:41.823749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.945
5-th percentile0
Q10
median0
Q36.433
95-th percentile91.5
Maximum898
Range899.945
Interquartile range (IQR)6.433

Descriptive statistics

Standard deviation113.77469
Coefficient of variation (CV)4.3745008
Kurtosis33.984303
Mean26.008611
Median Absolute Deviation (MAD)0
Skewness5.792076
Sum20442.768
Variance12944.68
MonotonicityNot monotonic
2023-01-03T11:52:42.046225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 449
56.7%
2.151 8
 
1.0%
1.035 6
 
0.8%
6.105 6
 
0.8%
698 4
 
0.5%
3.719 4
 
0.5%
4.5 4
 
0.5%
1.618 4
 
0.5%
100 4
 
0.5%
4.779 3
 
0.4%
Other values (245) 294
37.1%
(Missing) 6
 
0.8%
ValueCountFrequency (%)
-1.945 1
 
0.1%
-1 1
 
0.1%
0 449
56.7%
1 2
 
0.3%
1.035 6
 
0.8%
1.123 1
 
0.1%
1.305 2
 
0.3%
1.313 1
 
0.1%
1.483 1
 
0.1%
1.492 1
 
0.1%
ValueCountFrequency (%)
898 1
 
0.1%
895 1
 
0.1%
833 1
 
0.1%
803 1
 
0.1%
793 1
 
0.1%
792 1
 
0.1%
758 1
 
0.1%
735 2
0.3%
698 4
0.5%
687 1
 
0.1%

Total
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct787
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
14.029
 
3
35.710
 
3
33.772
 
2
4.555.424
 
1
106.947
 
1
Other values (782)
782 

Length

Max length9
Median length7
Mean length6.6767677
Min length6

Characters and Unicode

Total characters5288
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique784 ?
Unique (%)99.0%

Sample

1st row4.555.424
2nd row1.417.541
3rd row62.378
4th row144.119
5th row171.628

Common Values

ValueCountFrequency (%)
14.029 3
 
0.4%
35.710 3
 
0.4%
33.772 2
 
0.3%
4.555.424 1
 
0.1%
106.947 1
 
0.1%
32.013 1
 
0.1%
561.542 1
 
0.1%
35.054 1
 
0.1%
151.631 1
 
0.1%
3.121.140 1
 
0.1%
Other values (777) 777
98.1%

Length

2023-01-03T11:52:42.127077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14.029 3
 
0.4%
35.710 3
 
0.4%
33.772 2
 
0.3%
1.412.119 1
 
0.1%
245.584 1
 
0.1%
84.303 1
 
0.1%
62.378 1
 
0.1%
144.119 1
 
0.1%
171.628 1
 
0.1%
1.003.803 1
 
0.1%
Other values (777) 777
98.1%

Most occurring characters

ValueCountFrequency (%)
. 859
16.2%
1 650
12.3%
3 484
9.2%
2 460
8.7%
5 424
8.0%
6 421
8.0%
9 410
7.8%
0 401
7.6%
4 400
7.6%
7 400
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4429
83.8%
Other Punctuation 859
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 650
14.7%
3 484
10.9%
2 460
10.4%
5 424
9.6%
6 421
9.5%
9 410
9.3%
0 401
9.1%
4 400
9.0%
7 400
9.0%
8 379
8.6%
Other Punctuation
ValueCountFrequency (%)
. 859
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 859
16.2%
1 650
12.3%
3 484
9.2%
2 460
8.7%
5 424
8.0%
6 421
8.0%
9 410
7.8%
0 401
7.6%
4 400
7.6%
7 400
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 859
16.2%
1 650
12.3%
3 484
9.2%
2 460
8.7%
5 424
8.0%
6 421
8.0%
9 410
7.8%
0 401
7.6%
4 400
7.6%
7 400
7.6%

Interactions

2023-01-03T11:52:39.223155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:35.909333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.478402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.050142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.590267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.134714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.668885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.308288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.004220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.564749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.141035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.673720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.222692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.753168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.390329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.083111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.645167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.217482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.753546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.298116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.830069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.464663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.162395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.731513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.291811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.834202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.373216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.903354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.637552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.238463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.815301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.365997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.903294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.445309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.974953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.705961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.319262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.894909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.440090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.975203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.520083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.067514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.780229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.402153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:36.973756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:37.516130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.059157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:38.594743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T11:52:39.153481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-03T11:52:42.194434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
AñoHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 MbpsOTROSTrimestreProvincia
Año1.0000.310-0.0080.1420.1520.3250.7400.1330.000
HASTA 512 kbps0.3101.0000.0600.1840.1240.1110.2690.0000.313
+ 512 Kbps - 1 Mbps-0.0080.0601.0000.1510.167-0.0350.0500.0000.270
+ 6 Mbps - 10 Mbps0.1420.1840.1511.0000.4840.3060.1330.0000.317
+ 10 Mbps - 20 Mbps0.1520.1240.1670.4841.0000.2310.2000.0000.209
+ 20 Mbps - 30 Mbps0.3250.111-0.0350.3060.2311.0000.2660.0000.191
OTROS0.7400.2690.0500.1330.2000.2661.0000.0000.153
Trimestre0.1330.0000.0000.0000.0000.0000.0001.0000.000
Provincia0.0000.3130.2700.3170.2090.1910.1530.0001.000

Missing values

2023-01-03T11:52:39.897134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-03T11:52:40.053291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AñoTrimestreProvinciaHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotal
020221Buenos Aires31.59130.056313.382321.756290.127161.1833.381.04926.2804.555.424
120221Capital Federal527.0005.57539.91877.39061.05343.2891.188.0721.7171.417.541
220221Catamarca71.000456.0004.3867.0098.7733.76135.7152.20762.378
320221Chaco461.0001.09916.88821.23520.89813.01262.9467.580144.119
420221Chubut113.0001.67761.36931.85633.08013.87114.05515.607171.628
520221Córdoba100.00012.782165.922126.00973.96734.892577.02713.1041.003.803
620221Corrientes67.0004.02924.56325.66527.07710.45243.6317.094142.578
720221Entre Ríos107.0005.74550.07549.62042.29421.57881.75716.750267.926
820221Formosa97.000448.00024.1136.9456.613716.00015.028588.00054.548
920221Jujuy58.0001.76122.14116.32137.923576.00035.2872.442116.509
AñoTrimestreProvinciaHASTA 512 kbps+ 512 Kbps - 1 Mbps+ 1 Mbps - 6 Mbps+ 6 Mbps - 10 Mbps+ 10 Mbps - 20 Mbps+ 20 Mbps - 30 Mbps+ 30 MbpsOTROSTotal
78220141Neuquén4.133987.00077.14884.0001.5822.0220.083.958
78320141Río Negro4.6704.61884.30473.0001.0621.080.094.736
78420141Salta53.00019.67764.0617.192314.0000.000.091.297
78520141San Juan531.0002.00051.0560.0000.0000.000.051.589
78620141San Luis7.0003.00012.5440.0001.0000.020.012.557
78720141Santa Cruz161.0001.62524.9721.0001.0000.000.026.760
78820141Santa Fe8.456124.468345.22520.3286.84523.06680.0506.013
78920141Santiago Del Estero1.23410.53122.8172.422109.0000.000.037.113
79020141Tierra Del Fuego12.000607.00030.9026.0000.0000.000.031.527
79120141Tucumán6.00034.67283.21011.779362.0003.000.0130.032